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Systems Engineering Neural Networks

Machine learning with a “system” approach

What you’ll learn

  • Understand the basic of machine learning
  • Set up a simple neural network
  • Understand the basics of systems engineering
  • Put machine learning in a system context


  • College level education
  • An interest for systems engineering and AI


Systems Engineering and Artificial Neural Networks? In appearance strange bedfellows, but deceitfully so. Over our lives, we spend a long time thinking about making the right decisions, but we hardly stop and think about how we do it. We do know that our brain processes millions of data simultaneously, and sometimes we do not even realize that a β€œsynaptic journey” has taken place. Every day, we deal with problems that can be seen as optimizing complex β€œsystems”.

According to IBM β€œMachine learning is a branch of artificial intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy.”

Naturally, this short course cannot cover all the contents of the reference material, but will cover the key topics such as the definition and engineering of neural networks. Then it will progress to the system thinking approach, with questions to help you reflect on what you have learned and apply what you’ve learned to your own project. So what do you need to follow this course? Thirst for knowledge, resourcefulness because the subject is complex, and it requires you to be proactive and go online to fill the gaps by reading articles and other resources, and even follow other online courses on the same topic. And of course, a great deal of patience because, as said, the subject requires some time to be fully mastered.

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Who this course is for:

  • Students
  • Programmers
  • Engineers
  • Professionals

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Course content

4 sections β€’ 13 lectures β€’ 30m total lengthCollapse all sections

Introduction5 lectures β€’ 12min

  • Introduction01:14
  • How Machine learning can improve your work.3 questions
  • The structure of the course01:47
  • Machine Learning overview04:15
  • What are neural networks used for?1 question
  • What problems neural networks can solve?1 question
  • The essence of the system approach01:37
  • Neural network and the system approach3 questions
  • Philosophy of Artificial Neural Network02:49
  • Reflection4 questions

Engineering complexity3 lectures β€’ 5min

  • Life Cycle Stages (LCS)01:47
  • Reflection on LCS4 questions
  • The convergence of AI and SE01:37
  • SE4AI and AI4SE01:50
  • Neural Networks and Systems Engineering3 questions

Neural Network for computational decision making3 lectures β€’ 10min

  • ANN applications supported by SE approach01:26
  • Decision making1 question
  • Corrosion. Decision Making03:47
  • Reflection on Lecture 103 questions
  • Classification. Sport Management Tool (SMT)04:38

Conclusions2 lectures β€’ 2min

  • Conclusion01:01
  • It is time to spark your neural network5 questions
  • Follow up : SE, MBSE and further AI resources01:14

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